Every release pushes DataGenie in one of three directions: sharper Autonomous Insights, deeper HyperConnected coverage, or stronger Responsible AI. Each entry below is a complete release record — highlights up top, full details and walkthroughs inline.Documentation Index
Fetch the complete documentation index at: https://docs.datagenie.ai/llms.txt
Use this file to discover all available pages before exploring further.
Jump to a release
v4.12 · Apr 2026
Dual Timezone + Fiscal Calendar
v4.11.1 · Mar 27, 2026
Root Story depth handling
v4.11 · Mar 18, 2026
Multi Yhat + KPI Attribution
v4.10 · Mar 4, 2026
GO Onboarding + Deep Dive
v4.9 · Feb 2026
Smart Mode + IOE upgrades
v4.12
WisdomDashboardsScenario PlanningBusiness EventsAnomaly DetectionIOE
April 13, 2026 · Dual Timezone, Fiscal Calendar & Platform Enhancements
Dual Timezone, Fiscal Calendar & Platform-Wide Enhancements
Release 4.12 brings dual-timezone support, native fiscal calendars, a meaningfully upgraded Dashboards experience, and powerful new Wisdom capabilities — Scenario Planning and Business Events — that make insight consumption richer and more contextual.Highlights
Dual Timezone Support
Decouple ingestion and business-reporting timezones for accurate, DST-safe metric processing.
Fiscal Calendar
Analyze metrics using fiscal periods and quarters (4-4-5, 4-5-4) aligned to financial reporting.
Scenario Planning
Simulate future outcomes by adjusting drivers and comparing against baseline forecasts.
Business Events
Capture real-world context so Wisdom explains anomalies with recorded causes — not invented ones.
Dual Timezone Support
DataGenie now supports configuring two separate timezones per dataset — one for how raw data is ingested, and one for how business metrics are reported.Previously, all data was processed in a single timezone, which could cause misalignment between when data arrives and how it maps to business reporting periods. With this update, teams can define an Ingestion Timezone (matching the source data) and an Aggregation Timezone (matching business reporting), ensuring metrics are always accurate regardless of where data originates.Ingestion Timezone
Defines the timezone of raw source data. Used for efficient filtering when reading data.
Aggregation Timezone
Defines the timezone used for business reporting. All metrics, stories, and KPIs are computed in this timezone.
Changing timezone settings on an existing dataset will trigger a full reprocessing of historical data to ensure consistency. Existing datasets default to UTC for both timezones with no change in behavior unless explicitly configured.
Fiscal Period & Quarter Support
DataGenie now supports fiscal calendars, enabling analysis aligned with business-defined reporting structures. Users can work with fiscal periods and fiscal quarters (such as 4-4-5 or 4-5-4), ensuring consistent comparisons and alignment with financial reporting across all views.Dashboard Improvements
The dashboard has been significantly improved with better stability, a more refined interface, and a smarter Quick Look experience.The Quick Look selector now automatically scrolls to the active dashboard, and switching dashboards no longer overrides your default — you can explicitly set or remove it via the selector popup. The same behavior applies to Top Stories filter presets. Dashboards currently set as Quick Look are protected from accidental deletion.General improvements include fixes to the time period display in the dashboard header, cleaner widget behavior, and a refreshed design across selector popups throughout the homepage and Top Stories.
Scenario Planning (Forecast & Regression)
Scenario Planning lets users simulate future outcomes by adjusting key business drivers and instantly seeing their projected impact on KPIs.Instead of relying only on a baseline forecast, users can test what-if scenarios — such as changes in conversion rate, traffic, or bounce rate. Each scenario is recalculated in real time and compared against the baseline, helping teams evaluate trade-offs and identify the most effective levers before making decisions.Driver Adjustments
Adjust individual business drivers and instantly see their projected effect on target KPIs.
Baseline Comparison
Each scenario is overlaid against the baseline forecast for a clear before/after view.
Business Events
Business Events introduces a structured way to capture real-world context directly within the platform. Users can document key events — promotions, campaigns, operational disruptions, strategic changes — that may influence data trends.Events can be scoped to a specific dataset and viewed in either a calendar or list format. Each event captures a name, start and end date, and a description of the business context. Wisdom uses these events to better connect observed trends to real-world causes, improving the accuracy and relevance of its explanations.Create Events
Log promotions, campaigns, or disruptions with a name, date range, description, and optional dataset scope.
Calendar & List Views
Visualize events over time in a calendar view, or manage them in a structured list format.
IOE Delta Processing
Users can now control whether alerts surface only newly processed date periods or also include reprocessed historical dates.By default, alerts show only new delta periods, reducing noise and keeping focus on what is actually new. Additional improvements include timezone awareness and fiscal calendar support for subject date handling, along with a fix for a fallback plain-text message that was incorrectly appearing in alert emails.Multi AD — Import / Export Support
Users can now export AD Group configurations from an existing dataset and import them into a new or existing dataset. Anomaly detection models and configurations can be reused without manual setup, improving consistency and reducing onboarding effort across datasets.Wisdom Enhancements
- Fiscal Year Support
- Question UI & Chart Controls
Wisdom now understands fiscal calendars and allows users to ask questions and run forecasts aligned to fiscal periods. It automatically maps fiscal months, quarters, and years to the correct date ranges, ensuring analysis and projections match business reporting structures.
KPI Handling
- Bound Consistency
- Display Clarity
User-defined KPI limits (upper and lower bounds) are now consistently applied across Explorer, stories, and metric calculations. This ensures uniform behavior across all views and improves reliability of KPI interpretation.
Bug fixes
KPI Attribution Consistency
Fixed inconsistencies in contribution bars and value mapping to ensure accurate attribution representation across the tree view.
General Stability Fixes
Improvements across anomaly detection behavior and metric consistency to reduce edge-case failures.
Improved Root Story Depth Handling
Root story generation rules are now relaxed at lower depths. Previously, root stories were always restricted toConfigured Depth - 1. The -1 rule now applies only when the configured depth is strictly greater than 2.- Depths 1–2 — root stories generate up to the exact configured depth
- Depths 3+ — root stories generate up to
Configured Depth - 1
v4.11
Anomaly DetectionMulti YhatKPI AttributionNirvana
March 18, 2026 · Advanced Detection & Deeper Explainability
Advanced Detection & Deeper Explainability
Multi-baseline anomaly detection, tree-based attribution for derived KPIs, and reliability improvements that make detection more stable across long-horizon datasets.Highlights
Multi Yhat (Multi AD Groups)
Configure multiple detection groups per dataset, compare baselines, and surface insights independently per group.
KPI Attribution
Tree-based attribution for derived KPIs with actual, predicted, deviation values, and SQL visibility.
Seasonality Enhancements
Improved weekly, monthly, and quarterly detection plus better zero-value handling.
Nirvana Dataset Isolation
Dedicated Nirvana tab with inherited default AD Groups for consistent multi-source detection.
Multi Yhat (Multi AD Groups)
Multi Yhat introduces the ability to configure and manage multiple anomaly detection groups per dataset. This gives business users the flexibility to define their own baseline models alongside DataGenie’s built-in anomaly detection and surface insights separately for each group.Multiple AD Groups per Dataset
Configure and manage multiple AD groups with custom models and parameters per dataset.
Custom Default Models
Define any AD group as the default baseline used across Top Stories and Deep Dive.
Explorer Integration
Compare trends and predictions across multiple Yhat lines directly within Explorer.
Nirvana Support
Nirvana datasets automatically inherit default AD groups from their component datasets.
Configuring AD Groups
Users can configure AD groups both during dataset creation and when editing an existing dataset. The configuration screen lets you select or create a custom AD model, define its parameters, and mark one group as the default.Prediction Preview with ADAAS
ADAAS now supports group-based prediction, enabling prediction previews in the configuration screen for each Multi Yhat group.
Insight Segmentation by AD Group
Insights are surfaced separately for each AD group across Top Stories, Deep Dive, and Explorer. Each group maintains its own prediction baseline, ensuring accurate anomaly detection and ranking.KPI Attribution
KPI Attribution introduces a visual and analytical workflow to understand how derived KPIs are constructed and what drives their movement.- Tree View
- SQL Expression
Any derived KPI can be expanded into a hierarchical tree showing all contributing metrics. Each node displays attribution value, attribution percentage, and severity, so you can see how each component KPI contributes to the overall change.
KPI Attribution remains synchronized with the selected KPI across all Deep Dive tabs, ensuring consistent attribution insights during analysis.
Seasonality & Zero Handling
Anomaly Detection models now include enhanced seasonality detection for higher granularities — weekly, monthly, and quarterly time series — increasing model stability and anomaly accuracy across long-horizon datasets.Improved zero-value handling ensures that AD models do not learn from structural zero values. This prevents distorted baselines and improves detection accuracy in datasets where zero values represent non-operational or inactive periods.Nirvana Dataset Experience
Nirvana datasets now appear exclusively under the Nirvana tab in the dataset listing page. This improves discoverability and ensures clearer dataset categorization for Nirvana-enabled workflows.
Bug fixes
Bounds for Data Quality KPIs
Data quality KPIs are now bounded by default. Count metrics have a lower bound of
0, and sub-population metrics are bounded between 0 and 100.Notebook Rerun Option
The rerun option is no longer shown for newly created notebooks, preventing unintended executions before the notebook has run.
Dataset Table Search Fix
Resolved an issue where search within the dataset listing table was not returning accurate results.
Nirvana Dataset Listing
Nirvana datasets now appear only under the Nirvana tab, ensuring accurate categorization across the dataset listing page.
Smarter Onboarding & Insight Precision
The biggest update yet to the onboarding and analysis engine — GO makes dataset onboarding conversational, Change Analysis gets sharper, and anomaly detection handles sparse and seasonal series better.Highlights
GO (Guided Onboarding)
Sample → context → blueprint, with image upload support.
Contributor Sorting
Sort by % deviation in addition to % contribution for Change Analysis.
Deep Dive Extended History
Longer KPI history available in-place without leaving the story view.
AD Reliability
Monthly seasonality, stable runs, and fewer false anomalies on sparse series.
GO (Guided Onboarding)
GO is the fastest way to onboard a dataset into DataGenie. It removes the guesswork from onboarding and gets you to “insights-ready” significantly faster.
Image Upload Support
Users can upload images during GO to provide richer context for onboarding.
Improved Question Box UI
A cleaner, more guided input experience while interacting with the GO flow.
Enhancements
- Analytics
- Visualization
- Time & Axis
Contributor Sorting for Change Analysis — For non-rollable KPIs, contributors can now be sorted by % deviation in addition to % contribution, helping users prioritize contributors by the largest deviations first.
AD Reliability
- Monthly Seasonality — Trackers with clear monthly patterns are better handled so seasonality is captured more reliably. Better baseline fit and reduced missed pattern-driven behavior.
- Run Reliability — AD jobs are more robust in cases where predictions or sigma were intermittently null. Fixes a root cause that led to empty prediction patches.
- Sparse Series Handling — AD is now less sensitive to imputation side-effects on sparse trackers with occasional spikes, reducing false anomalies.
Fixes
Root Story Output
When no View Filter is applied, only impacted KPIs are shown and blank-value KPIs are excluded.
Derived KPI Metrics
Metrics fetching now returns correct results for derived KPIs across multiple granularities.
Weekly Alignment
Weekly start-day alignment is now consistent to prevent mismatched week periods in insights.
Dimension Edits
Display Name edits now persist correctly and no longer disappear during the configuration flow.
Dashboard Reliability — Dashboard deletion no longer fails due to backend exceptions or constraints.
Other changes
- Platform improvements — Improved caching and storage behavior for Airflow and insights reliability.
- Resource allocation — Increased resources for Insights and Metrics services to reduce failures under load.
- Stability — Improved Airflow pod restart stability and updated runtime components.
- Databricks support — Added support for configurable cluster allocation strategies for Databricks workloads.
Smarter Stories & Stronger Reliability
Sharper Top Stories relevance via Smart Mode, broader IOE controls, and reliability improvements that keep insights fresh and stable.Highlights
Smart Mode
Wisdom decides KPI ordering, highlights a Hero KPI, and groups Relevant vs Other KPIs for faster scanning.
IOE Upgrades
Alert toggle, authorized dataset selection, refreshed templates, batch mode, and a no-stories fallback.
Impact Score Alignment
Ranking adapts to KPIs selected in View Filter; Business KPI weightage removed for simpler behavior.
Clearer Explainability
Fallback when drivers are missing; richer contributor tooltips with deviation context.
Smart Mode (Wisdom) for Top Stories
Smart Mode changes how KPIs are presented inside each Top Story card so users can scan faster and focus on what matters first.- Smart Mode OFF — KPI ordering follows Impact Score ordering logic.
- Smart Mode ON — Wisdom decides KPI order using severity, business impact, and domain knowledge.
Hero KPI
The most important KPI becomes the Hero KPI.
Relevant KPIs
KPIs strongly related to the Hero KPI appear under Relevant KPIs.
Other KPIs
Less significant or loosely related KPIs are grouped under Other KPIs.
Behavior change — In Smart Mode, story cards show all KPIs and the View Filter is not honored, because Smart Mode decides what to show and in what order.
Enhancements
- IOE (Insights over Email)
- Top Stories and ranking
- Knowledge Base
- Filters and charts
- Deep Dive UX
Alert channel toggle UI — Alerts can be paused/resumed without deleting the alert.
Bug fixes
Overview KPI persistence
Selecting a new metric in Deep Dive no longer changes the primary KPI in Overview. Overview stays locked to the original insight while exploration continues.
Last period ingestion fix
Time-filtering no longer excludes the final time bucket. Top Stories now processes the latest hour/day correctly.
Daily datasets: seasonality improvements
Better weekly seasonality detection for daily datasets.
Quarterly datasets: model and overfitting fix
Improved models for low-sample quarterly series and fixed tracker overfitting so anomalies aren’t missed.
IOE dataset permission enforcement
Users can no longer configure or view alerts for unauthorized datasets (enforced via dataset selector).
Stable screenshot capture
Email screenshots are captured only after the page fully renders, reducing blank or partial screenshots.
Other changes
- Automatic database patches (DGRM client) — DGRM client can auto-install required DB scripts/delta versions during releases to reduce manual patching.
- Ingress class name configurable — Previously defaulted to NGINX; now controlled via an environment variable to match the environment’s ingress class.
- Insights Service cache volume — Cache no longer resets on pod restarts/deployments, reducing warm-up after releases.
- Pipeline/Airflow — When older periods are recomputed and values change, the pipeline clears insights cache so stories refresh and reflect the latest values.